small update

README.md

@@ -37,7 +37,7 @@ Both models used the same hyperparameters during training.\
 `per_device_train_batch_size=2`:\
 `gradient_accumulation_steps=4`: The number of steps to accumulate gradients before performing a backpropagation update. Higher accumulates gradients over multiple steps, increasing the batch size without requiring additional memory. Can improve training stability and convergence if you have a large model and limited hardware.\
 `learning_rate=2e-4`: Rate at which the model updates its parameters during training. Higher gives faster convergence but risks overshooting optimal parameters and instability. Lower requires more training steps but better performance.\
-`optim="adamw_8bit"`\: Using the Adam optimizer, a gradient descent method with momentum.
+`optim="adamw_8bit"`\: Using the Adam optimizer, a gradient descent method with momentum.\
 `weight_decay=0.01`: Penalty to add to the weights during training to prevent overfitting. The value is proportional to the magnitude of the weights to the loss function.\
 `lr_scheduler_type="linear"`: We decrease the learning rate linearly.